Add InternVL3 support

README.md

@@ -263,6 +263,7 @@ loss.backward()
 | Granite 3.0 & 3.1   | `liger_kernel.transformers.apply_liger_kernel_to_granite`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss |
 | OLMo2   | `liger_kernel.transformers.apply_liger_kernel_to_olmo2`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy |
 | GLM-4   | `liger_kernel.transformers.apply_liger_kernel_to_glm4`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy |
+| InternVL3   | `liger_kernel.transformers.apply_liger_kernel_to_internvl`     | RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy |
 
 
 ## Low-level APIs

src/liger_kernel/transformers/__init__.py

@@ -38,6 +38,7 @@
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_glm4v  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_glm4v_moe  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_granite  # noqa: F401
+    from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_internvl  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_llama  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_llama4  # noqa: F401
     from liger_kernel.transformers.monkey_patch import apply_liger_kernel_to_llava  # noqa: F401
@@ -98,6 +99,7 @@ def __getattr__(name: str):
         "apply_liger_kernel_to_glm4v",
         "apply_liger_kernel_to_glm4v_moe",
         "apply_liger_kernel_to_granite",
+        "apply_liger_kernel_to_internvl",
         "apply_liger_kernel_to_llama",
         "apply_liger_kernel_to_llava",
         "apply_liger_kernel_to_llama4",
@@ -163,6 +165,7 @@ def __getattr__(name: str):
             "apply_liger_kernel_to_glm4v",
             "apply_liger_kernel_to_glm4v_moe",
             "apply_liger_kernel_to_granite",
+            "apply_liger_kernel_to_internvl",
             "apply_liger_kernel_to_llama",
             "apply_liger_kernel_to_llava",
             "apply_liger_kernel_to_llama4",

src/liger_kernel/transformers/model/internvl.py

@@ -0,0 +1,150 @@
+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import torch
+
+from transformers.models.internvl.modeling_internvl import InternVLCausalLMOutputWithPast
+from transformers.utils import can_return_tuple
+
+from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
+
+
+# Copied from https://github.com/huggingface/transformers/blob/d888bd435d0c0eaabaabad5b33d52af518c7187c/src/transformers/models/internvl/modeling_internvl.py#L862
+@can_return_tuple
+def lce_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    pixel_values: Optional[torch.FloatTensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    vision_feature_layer: Optional[Union[int, List[int]]] = None,
+    vision_feature_select_strategy: Optional[str] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    cache_position: Optional[torch.LongTensor] = None,
+    logits_to_keep: Union[int, torch.Tensor] = 0,
+    image_sizes: Optional[torch.Tensor] = None,
+    skip_logits: Optional[bool] = None,  # Added argument for liger-kernel
+    **lm_kwargs,  # renamed from kwargs
+) -> Union[Tuple, InternVLCausalLMOutputWithPast]:
+    r"""
+    Example:
+
+    ```python
+    >>> import torch
+    >>> from transformers import AutoProcessor, AutoModelForImageTextToText
+
+    >>> torch_device = "cuda"
+    >>> processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL3-1B-hf")
+    >>> model = AutoModelForImageTextToText.from_pretrained(
+    ...     "OpenGVLab/InternVL3-1B-hf", dtype=torch.bfloat16, device_map=torch_device
+    ... )
+
+    >>> messages = [
+    ...     {
+    ...         "role": "user",
+    ...         "content": [
+    ...             {
+    ...                 "type": "image",
+    ...                 "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
+    ...             },
+    ...             {
+    ...                 "type": "image",
+    ...                 "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
+    ...             },
+    ...             {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
+    ...         ],
+    ...     },
+    ... ]
+
+    >>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device)
+    >>> generate_ids = model.generate(**inputs, max_new_tokens=200)
+    >>> print(processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))
+    The images depict the Statue of Liberty and the Golden Gate Bridge.
+    ```"""
+
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+    vision_feature_layer = (
+        vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+    )
+    vision_feature_select_strategy = (
+        vision_feature_select_strategy
+        if vision_feature_select_strategy is not None
+        else self.config.vision_feature_select_strategy
+    )
+
+    outputs = self.model(
+        input_ids=input_ids,
+        pixel_values=pixel_values,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        vision_feature_layer=vision_feature_layer,
+        vision_feature_select_strategy=vision_feature_select_strategy,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        cache_position=cache_position,
+        image_sizes=image_sizes,
+        **lm_kwargs,
+    )
+
+    # Copied from llava.py
+    hidden_states = outputs[0]
+    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+    slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+    kept_hidden_states = hidden_states[:, slice_indices, :]
+
+    shift_labels = lm_kwargs.pop("shift_labels", None)
+    logits = None
+    loss = None
+
+    if skip_logits and labels is None and shift_labels is None:
+        raise ValueError("skip_logits is True, but labels and shift_labels are None")
+
+    if skip_logits is None:
+        # By default, if in training mode, don't materialize logits
+        skip_logits = self.training and (labels is not None or shift_labels is not None)
+
+    if skip_logits:
+        loss = LigerForCausalLMLoss(
+            hidden_states=kept_hidden_states,
+            lm_head_weight=self.lm_head.weight,
+            labels=labels,
+            shift_labels=shift_labels,
+            hidden_size=self.config.text_config.hidden_size,
+            **lm_kwargs,
+        )
+
+    else:
+        logits = self.lm_head(kept_hidden_states)
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **lm_kwargs
+            )
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return InternVLCausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+        image_hidden_states=outputs.image_hidden_states,
+    )

src/liger_kernel/transformers/monkey_patch.py

@@ -4,6 +4,7 @@
 from functools import partial
 from types import MethodType
 from typing import Callable
+from typing import Optional
 
 import transformers
 
@@ -1334,7 +1335,6 @@ def apply_liger_kernel_to_qwen2(
             if rms_norm:
                 _patch_rms_norm_module(decoder_layer.input_layernorm)
                 _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
-    print("Applied Liger kernels to Qwen2")
 
 
 def apply_liger_kernel_to_qwen3(
@@ -2029,6 +2029,84 @@ def apply_liger_kernel_to_glm4v_moe(
                     _patch_rms_norm_module(decoder_layer.post_attention_layernorm)
 
 
+def apply_liger_kernel_to_internvl(
+    cross_entropy: bool = False,
+    fused_linear_cross_entropy: bool = True,
+    rms_norm: bool = True,
+    model: Optional[PreTrainedModel] = None,
+    **kwargs,
+) -> None:
+    """
+    Apply Liger kernels to replace original implementation in HuggingFace InternVL models.
+    Due to the characteristics of InternVL, the model must be passed to apply Liger-Kernel's patch to other models connected to InternVL.
+    However, if an LM not supported by Liger-Kernel is connected to InternVL, unexpected side effects may occur.
+    NOTE: InternVL is not available in transformers<4.52.1
+
+    Args:
+        rope (bool): Whether to apply Liger's rotary position embedding. Default is True.
+        cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+        fused_linear_cross_entropy (bool):
+            Whether to apply Liger's fused linear cross entropy loss. Default is True.
+            `cross_entropy` and `fused_linear_cross_entropy` cannot both be True.
+            If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+        rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
+        swiglu (bool): Whether to apply Liger's SwiGLU MLP. Default is True.
+        model (PreTrainedModel): The model instance to apply Liger kernels to, if the model has already been
+        loaded. Default is None.
+    """
+    assert not (cross_entropy and fused_linear_cross_entropy), (
+        "cross_entropy and fused_linear_cross_entropy cannot both be True."
+    )
+
+    from transformers.models.internvl import modeling_internvl
+
+    from liger_kernel.transformers.model.internvl import lce_forward as internvl_lce_forward
+
+    if cross_entropy:
+        logger.warning(TRANSFORMER_DEPRECATION_WARNING)
+        modeling_internvl.nn.CrossEntropyLoss = LigerCrossEntropyLoss
+    if fused_linear_cross_entropy:
+        modeling_internvl.InternVLForConditionalGeneration.forward = internvl_lce_forward
+    if rms_norm:
+        modeling_internvl.InternVLVisionRMSNorm = LigerRMSNorm
+
+    if model is not None:
+        text_model_name, vision_model_name = model.config.text_config.model_type, model.config.vision_config.model_type
+        text_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN.get(text_model_name, None)
+        vision_liger_fn = MODEL_TYPE_TO_APPLY_LIGER_FN.get(vision_model_name, None)
+
+        kwargs = {"cross_entropy": False, "fused_linear_cross_entropy": False, **kwargs} | {"rms_norm": rms_norm}
+        if text_liger_fn:
+            accept_params = inspect.signature(text_liger_fn).parameters
+            remain_params = set(kwargs) - (set(accept_params) & set(kwargs))
+            text_kwargs = {k: v for k, v in kwargs.items() if k not in remain_params}
+
+            if remain_params:
+                logger.warning(
+                    f"These parameters are not supported by {text_model_name}. Enter the remaining {list(text_kwargs.keys())} except for {list(remain_params)}\n"
+                    f"Parameters accepted by {text_model_name}: {list(accept_params.keys())}"
+                )
+            text_kwargs["model"] = model.language_model
+            text_liger_fn(**text_kwargs)
+        elif text_model_name not in MODEL_TYPE_TO_APPLY_LIGER_FN:
+            logger.warning(f"{text_model_name} is not supported by Liger kernel.")
+
+        if vision_liger_fn:
+            accept_params = inspect.signature(vision_liger_fn).parameters
+            remain_params = set(kwargs) - (set(accept_params) & set(kwargs))
+            vision_kwargs = {k: v for k, v in kwargs.items() if k not in remain_params}
+
+            if remain_params:
+                logger.warning(
+                    f"These parameters are not supported by {vision_model_name}. Enter the remaining {list(vision_kwargs.keys())} except for {list(remain_params)}\n"
+                    f"Parameters accepted by {vision_model_name}: {list(accept_params.keys())}"
+                )
+            vision_kwargs["model"] = model.vision_tower
+            vision_liger_fn(**vision_kwargs)
+        elif vision_model_name not in MODEL_TYPE_TO_APPLY_LIGER_FN:
+            logger.warning(f"{vision_model_name} is not supported by Liger kernel.")
+
+
 # Model type corresponds to the keys defined in transformers/models/auto/modeling_auto.py
 MODEL_TYPE_TO_APPLY_LIGER_FN = {
     "gemma": apply_liger_kernel_to_gemma,
@@ -2038,6 +2116,7 @@ def apply_liger_kernel_to_glm4v_moe(
     "glm4": apply_liger_kernel_to_glm4,
     "glm4v": apply_liger_kernel_to_glm4v,
     "glm4v_moe": apply_liger_kernel_to_glm4v_moe,
+    "internvl": apply_liger_kernel_to_internvl,
     "llama": apply_liger_kernel_to_llama,
     "llama4_text": apply_liger_kernel_to_llama4,
     "llama4": apply_liger_kernel_to_llama4,

test/convergence/bf16/test_mini_models.py

@@ -25,6 +25,7 @@
 from liger_kernel.transformers import apply_liger_kernel_to_glm4v
 from liger_kernel.transformers import apply_liger_kernel_to_glm4v_moe
 from liger_kernel.transformers import apply_liger_kernel_to_granite
+from liger_kernel.transformers import apply_liger_kernel_to_internvl
 from liger_kernel.transformers import apply_liger_kernel_to_llama
 from liger_kernel.transformers import apply_liger_kernel_to_llama4
 from liger_kernel.transformers import apply_liger_kernel_to_llava
@@ -51,6 +52,7 @@
 from test.utils import revert_liger_kernel_to_glm4v
 from test.utils import revert_liger_kernel_to_glm4v_moe
 from test.utils import revert_liger_kernel_to_granite
+from test.utils import revert_liger_kernel_to_internvl
 from test.utils import revert_liger_kernel_to_llama
 from test.utils import revert_liger_kernel_to_llama4
 from test.utils import revert_liger_kernel_to_llava
@@ -190,6 +192,15 @@
 except ImportError:
     SMOLLM3_AVAILABLE = False
 
+try:
+    # InternVL is only available in transformers>=4.52.1
+    from transformers.models.internvl.configuration_internvl import InternVLConfig
+    from transformers.models.internvl.modeling_internvl import InternVLForConditionalGeneration
+
+    INTERNVL_AVAILABLE = True
+except ImportError:
+    INTERNVL_AVAILABLE = False
+
 from liger_kernel.utils import infer_device
 
 device = infer_device()
@@ -1022,6 +1033,38 @@
         ),
     )
 
+if INTERNVL_AVAILABLE:
+    MINI_MODEL_SETUPS["mini_internvl"] = MiniModelConfig(
+        liger_kernel_patch_func=apply_liger_kernel_to_internvl,
+        liger_kernel_patch_revert_func=revert_liger_kernel_to_internvl,
+        model_class=InternVLForConditionalGeneration,
+        mini_model_config=InternVLConfig(
+            text_config=Qwen2Config(
+                rms_norm_eps=1e-5,
+                hidden_size=256,  # 1024
+                intermediate_size=1024,  # 4096
+                hidden_act="silu",
+                num_hidden_layers=4,  # 24
+                num_attention_heads=4,  # 16
+                num_key_value_heads=2,  # 16
+                max_position_embeddings=4096,  # 8192
+                vocab_size=32000,  # 151936
+                bos_token_id=1,
+                eos_token_id=2,
+                pad_token_id=2,
+                tie_word_embeddings=False,
+            ),
+            vision_config={
+                "hidden_size": 256,  # 1024
+                "intermediate_size": 1024,  # 4096
+                "num_hidden_layers": 4,  # 24
+                "num_attention_heads": 4,  # 16
+            },
+            image_token_id=10,
+            attn_implementation="sdpa",  # default value, pytorch native attention
+        ),
+    )
+
 
 def create_model(model_name="mini_llama4"):
     """
@@ -1080,6 +1123,8 @@ def run_mini_model(
     else:
         MINI_MODEL_SETUPS[model_name].liger_kernel_patch_revert_func(**revert_kwargs)
 
+    print(MINI_MODEL_SETUPS[model_name])
+
     model = create_model(model_name).to(dtype).to(device)
 
     train_dataset = load_from_disk(DEFAULT_DATASET_PATH)
@@ -1432,6 +1477,25 @@ def run_mini_model(
                 ),
             ],
         ),
+        pytest.param(
+            "mini_internvl",
+            32,
+            1e-4,
+            torch.bfloat16,
+            1e-3,
+            1e-2,
+            1e-1,
+            1e-2,
+            1e-2,
+            1e-2,
+            marks=[
+                pytest.mark.skipif(not supports_bfloat16(), reason="bfloat16 not supported on this GPU"),
+                pytest.mark.skipif(
+                    not INTERNVL_AVAILABLE,
+                    reason="InternVL not available in this version of transformers",
+                ),
+            ],
+        ),
         # TODO: mixtral is flaky so disable the test for now
         # pytest.param(
         #     "mini_mixtral",